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Diagnostic support tools based on artificial intelligence (AI) have exhibited high performance in various medical fields. However, their clinical application remains challenging because of the lack of explanatory power in AI decisions (black box problem), making it difficult to build trust with medical professionals. Nevertheless, visualizing the internal representation of deep neural networks will increase explanatory power and improve the confidence of medical professionals in AI decisions. We propose a novel deep learning-based explainable representation “graph chart diagram” to support fetal cardiac ultrasound screening, which has low detection rates of congenital heart diseases due to the difficulty in mastering the technique. Screening performance improves using this representation from 0.966 to 0.975 for experts, 0.829 to 0.890 for fellows, and 0.616 to 0.748 for residents in the arithmetic mean of area under the curve of a receiver operating characteristic curve. This is the first demonstration wherein examiners used deep learning-based explainable representation to improve the performance of fetal cardiac ultrasound screening, highlighting the potential of explainable AI to augment examiner capabilities.

Wireless capsule endoscopy is a non-invasive and painless procedure to examine the gastrointestinal tract of human body, and an experienced clinician takes 2–3 hours for complete examination. To reduce this diagnosis time, the present work proposes a lightweight CNN model for binary classification of WCE images. The proposed model has a strong backbone of CNN in the primary branch complemented by resolution preserving dilated convolution layers in secondary branches. The proposed model extracts multiple features at different scales and finally fuses them together to fetch the dominant global feature that aids in binary classification problem. A new dataset has been created in collaboration with All India Institute of Medical Sciences, Delhi. The efficacy of the proposed model has been verified using the developed dataset using various subjective and objective parameters. Feature maps generated at each branch have been thoroughly analyzed to understand the quality of learning. Thorough experimental analysis indicates that the proposed model yields an accuracy of 0.96, sensitivity of 0.93 and specificity of 0.97 on real data collected from AIIMS Delhi. To verify the efficacy of the proposed dilated CNN, extensive analysis has been done using standard KID dataset as well. For a fair comparison, these datasets have also been used for pre-trained inception net model. Thorough analysis indicates that the proposed architecture performs well both for AIIMS dataset and the standard KID dataset. Result analysis also reflects that the proposed dilated CNN architecture outperforms the performance of pre-trained inception net model.

Purpose Pregnancy loss ranging from spontaneous abortion (SAB) to stillbirth can result from monogenic causes of Mendelian inheritance. This study evaluated the clinical application of exome sequencing (ES) in identifying the genetic etiology for pregnancy loss. Methods A cohort of 102 specimens from products of conception (POC) with normal karyotype and absence of pathogenic copy-number variants were selected for ES. Abnormality detection rate (ADR) and variants of diagnostic value correlated with SAB and stillbirth were evaluated. Results ES detected 6 pathogenic variants, 16 likely pathogenic variants, and 17 variants of uncertain significance favor pathogenic (VUSfp) from this cohort. The ADR for pathogenic and likely pathogenic variants was 22% and reached 35% with the inclusion of VUSfp. The ADRs of SAB and stillbirth were 36% and 33%, respectively. Affected genes included those associated with multisystem abnormalities, neurodevelopmental disorders, cardiac anomalies, skeletal dysplasia, metabolic disorders, and renal diseases. Conclusion These results supported the clinical utility of ES for detecting monogenic etiology of pregnancy loss. The identification of disease-associated variants provided information for follow-up genetic counseling of recurrence risk and management of subsequent pregnancies. Discovery of novel variants could provide insight for underlying molecular mechanisms causing fetal death.

This study proposes a novel multi-network architecture consisting of a multi-scale convolution neural network (MSCNN) with fully connected graph convolution network (GCN), named MSCNN-GCN, for the detection of musculoskeletal abnormalities via musculoskeletal radiographs. To obtain both detailed and contextual information for a better description of the characteristics of the radiographs, the designed MSCNN contains three subnetwork sequences (three different scales). It maintains high resolution in each sub-network, while fusing features with different resolutions. A GCN structure was employed to demonstrate global structure information of the images. Furthermore, both the outputs of MSCNN and GCN were fused through the concat of the two feature vectors from them, thus making the novel framework more discriminative. The effectiveness of this model was verified by comparing the performance of radiologists and three popular CNN models (DenseNet169, CapsNet, and MSCNN) with three evaluation metrics (Accuracy, F1 score, and Kappa score) using the MURA dataset (a large dataset of bone X-rays). Experimental results showed that the proposed framework not only reached the highest accuracy, but also demonstrated top scores on both F1 metric and kappa metric. This indicates that the proposed model achieves high accuracy and strong robustness in musculoskeletal radiographs, which presents strong potential for a feasible scheme with intelligent medical cases.

Fluid viscous dampers (FVDs) in long‐span bridges are prone to performance change, in which the gap effect caused by oil leakage and the parameter alteration induced by viscous material denaturation are two primary sources of change. These variations may negatively affect the safety of both the bridge and the damper, thus underlining the significance of performance assessment and abnormality detection. This study develops a Gap‐Maxwell (G‐M) model to simulate the restoring force characteristics of the FVD considering performance alteration and subsequently suggests identification methods for gap and parameter change to capture the condition variation of the damper. The G‐M model contains a gap–hook element group and a Maxwell element, where the gap length of the gap element represents the leakage, and the parameter change is achieved by setting different parameter values for the Maxwell element. Its feasibility is verified by comparison with the cyclic test results. The simplified longitudinal movement pattern for the railway suspension bridge during the operational stage is suggested. Based on the G‐M model and the movement pattern, the segmental gap identification (SGI) method is proposed to determine the gap length by segmenting the original data and identifying the gap in each segment. Numerical simulations illustrate its accuracy and robustness under different damper parameter settings and noise pollution. The G‐M model parameter identification (GMPI) procedure is raised to capture the parameter change, which follows a procedure of preprocessing, clustering, fitting, and optimization. It is numerically proved to be effective in identifying the damping coefficient and velocity exponent of the FVD.

The ability to identify and accurately predict abnormal behavior is important for health monitoring systems in smart environments. Specifically, for elderly persons wishing to maintain their independence and comfort in their living spaces, abnormal behaviors observed during activities of daily living are a good indicator that the person is more likely to have health and behavioral problems that need intervention and assistance. In this paper, we investigate a variety of deep learning models such as Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM and Autoencoder-CNN-LSTM for identifying and accurately predicting the abnormal behaviors of elderly people. The temporal information and spatial sequences collected over time are used to generate models, which can be fitted to the training data and the fitted model can be used to make a prediction. We present an experimental evaluation of these models performance in identifying and predicting elderly persons abnormal behaviors in smart homes, via extensive testing on two public data sets, taking into account different models architectures and tuning the hyperparameters for each model. The performance evaluation is focused on accuracy measure.

Nailfold Capillaroscopy (NFC) is a simple, non-invasive diagnostic tool used to detect microvascular changes in nailfold. Chronic pathological changes associated with a wide range of systemic diseases, such as diabetes, cardiovascular disorders, and rheumatological conditions like systemic sclerosis, can manifest as observable microvascular changes in the terminal capillaries of nailfolds. The current gold standard relies on experts performing manual evaluations, which is an exhaustive time-intensive, and subjective process. In this study, we demonstrate the viability of a deep learning approach as an automated clinical screening tool. Our dataset consists of NFC images from a total of 225 participants, with normal images accounting for 6% of the dataset. This study introduces a robust framework utilizing cascade transfer learning based on EfficientNet-B0 to differentiate between normal and abnormal cases within NFC images. The results demonstrate that pre-trained EfficientNet-B0 on the ImageNet dataset, followed by transfer learning from domain-specific classes, significantly enhances the classifier’s performance in distinguishing between Normal and Abnormal classes. Our proposed model achieved superior performance, with accuracy, precision, recall, F1 score, and ROC_AUC of 1.00, significantly outperforming both models of single transfer learning on the pre-trained EfficientNet-B0 and cascade transfer learning on a convolutional neural network, which each attained an accuracy, precision, recall, and F1 score of 0.67 and a ROC_AUC of 0.83. The framework demonstrates the potential to facilitate early preventive measures and timely interventions that aim to improve healthcare delivery and patients’ quality of life.

Truck hoisting detection constitutes a key focus in port security, for which no optimal resolution has been identified. To address the issues of high costs, susceptibility to weather conditions, and low accuracy in conventional methods for truck hoisting detection, a non-intrusive detection approach is proposed in this paper. The proposed approach utilizes a mathematical model and an extreme gradient boosting (XGBoost) model. Electrical signals, including voltage and current, collected by Hall sensors are processed by the mathematical model, which augments their physical information. Subsequently, the dataset filtered by the mathematical model is used to train the XGBoost model, enabling the XGBoost model to effectively identify abnormal hoists. Improvements were observed in the performance of the XGBoost model as utilized in this paper. Finally, experiments were conducted at several stations. The overall false positive rate did not exceed 0.7% and no false negatives occurred in the experiments. The experimental results demonstrated the excellent performance of the proposed approach, which can reduce the costs and improve the accuracy of detection in container hoisting.

We propose a novel framework of using a nonparametric Bayesian model, called Dual Hierarchical Dirichlet Processes (Dual-HDP) (Wang et al. in IEEE Trans. Pattern Anal. Mach. Intell. 31:539–555, 2009 ), for unsupervised trajectory analysis and semantic region modeling in surveillance settings. In our approach, trajectories are treated as documents and observations of an object on a trajectory are treated as words in a document. Trajectories are clustered into different activities. Abnormal trajectories are detected as samples with low likelihoods. The semantic regions, which are subsets of paths commonly taken by objects and are related to activities in the scene, are also modeled. Under Dual-HDP, both the number of activity categories and the number of semantic regions are automatically learnt from data. In this paper, we further extend Dual-HDP to a Dynamic Dual-HDP model which allows dynamic update of activity models and online detection of normal/abnormal activities. Experiments are evaluated on a simulated data set and two real data sets, which include 8,478 radar tracks collected from a maritime port and 40,453 visual tracks collected from a parking lot.

In this paper, we provide a comprehensive survey of the recent advances in abnormality detection in smart grids using multimodal image data, which include visible light, infrared, and optical satellite images. The applications in visible light and infrared images, enabling abnormality detection at short range, further include several typical applications in intelligent sensors deployed in smart grids, while optical satellite image data focus on abnormality detection from a large distance. Moreover, the literature in each aspect is organized according to the considered techniques. In addition, several key methodologies and conditions for applying these techniques to abnormality detection are identified to help determine whether to use deep learning and which kind of learning techniques to use. Traditional approaches are also summarized together with their performance comparison with deep-learning-based approaches, based on which the necessity, seen in the surveyed literature, of adopting image-data-based abnormality detection is clarified. Overall, this comprehensive survey categorizes and carefully summarizes insights from representative papers in this field, which will widely benefit practitioners and academic researchers.